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import matplotlib.pyplot as plt
import numpy as np
import matplotlib.cm as cm
from utils.util_data import load_dataset
def get_cmap(num_colors):
if num_colors <= 10:
cm_name = "tab10"
elif num_colors <= 20:
cm_name = "tab20"
else:
assert False
return cm.get_cmap(cm_name)
def analyze_dataset(dataset_path, output_dir):
dataset = load_dataset(dataset_path)
#-----------------------------
# Stepwise frequency analysis
#-----------------------------
max_steps = len(dataset[0][0]) # num_nodes
num_labels = 2
freq = [[] for _ in range(num_labels)]
weights = [[] for _ in range(num_labels)]
for instance in dataset:
labels = instance[-1]
for step, label in labels:
freq[label].append(step)
# visualize histogram
fig = plt.figure(figsize=(10, 10))
binwidth = 1
bins = np.arange(0, max_steps + binwidth, binwidth)
cmap = get_cmap(num_labels)
for i in range(len(weights)):
weights[i] = np.ones(len(freq[i])) / len(dataset)
plt.hist(freq[i], bins=bins, alpha=0.5, weights=weights[i], ec=cmap(i), color=cmap(i), label="prioritizing tour length", align="left")
plt.xlabel("Steps")
plt.ylabel("Frequency (density)")
if max_steps <= 20:
plt.xticks(np.arange(0, max_steps+1, 1))
plt.title(f"# of samples = {len(dataset)}\n# of nodes = {max_steps}")
plt.legend()
plt.savefig(f"{output_dir}/hist.png", dpi=150, bbox_inches="tight")
#-----------------------------
# Overall ratio of each class
#-----------------------------
total = np.sum([len(freq[i]) for i in range(num_labels)])
ratio = np.array([len(freq[i]) for i in range(num_labels)])
ratio = ratio / total
with open(f"{output_dir}/ratio.dat", "w") as f:
for i in range(len(ratio)):
print(f"label{i}, {ratio[i]}", file=f)
if __name__ == "__main__":
import argparse
import os
parser = argparse.ArgumentParser(description='')
parser.add_argument("--dataset_path", type=str, required=True)
parser.add_argument("--output_dir", type=str, default=None)
args = parser.parse_args()
if args.output_dir is None:
dataset_dir = os.path.split(args.dataset_path)[0]
output_dir = dataset_dir
else:
output_dir = args.output_dir
output_dir += "/analysis"
os.makedirs(output_dir, exist_ok=True)
analyze_dataset(args.dataset_path, output_dir) |